An EMS Medley

August 8, 2008

Agenda

• Background• Case Studies• Key Findings

EMS Projects 2001-2008

• Edmonton Single Start Station – Simulation• Station Location – St Albert• Performance Improvement – Calgary • Flexing/SSM/Redeployment – Edmonton/Calgary• Regionalization – Calgary & Edmonton Regions• Call Forecasting – Seattle

Sources of Reduced Performance

Structural

– Road congestion– Call growth– Suburban sprawl– Hospital Locations

Strategic Operational

– Station location– Long hospital waits– Insufficient units– Insufficient station

capacity– Insufficient

stations– Insufficient crews

– Poor scheduling– Long setup times– Long patient times– Redeployment gaps– Improper call

evaluation– Poor dispatch policies– Poor redeployment

policies

Data Available

• Call records (time stamps, priority, locations)• Event records (shift starts and ends, moves)• Scheduled units• AVL (10 second location and state data)

• Messy…

459,501

Calgary Project – Performance Improvement

Calgary EMS – How Can we Improve?• 2005 Population – ~950K (2.4% growth)• 24 current stations • 721 km2 geographic area• Up to 42 ambulances at peak time• ~15,000 P1 calls - ~95,000 total calls in last year

Overall Performance

81%82%81%

77% 76%

50%

55%

60%

65%

70%

75%

80%

85%

90%

95%

100%

2000 2001 2002 2003 2004

% Response < 8min

Performance

Call growth

Weekly Call Volumes

0

200

400

600

800

1000

1200

1400

1600

1800

2000 2001 2002 2003

Calls

Calls have been growing at a 9.8% compound annual rate since 2000.

Population (000s) – 1.6% CAGR

EMS Calls – 9.8% CAGR

Call patterns – GeographicCall growth has been concentrated in the periphery.

0

200

400

600

800

1000

1200

2000 2001 2002 2003 2004

Downtown

Periphery

Priority 1 calls from all years.

Growth

8.5%

5.8%

EMS Call Growth

Call Forecasting

Response Times

The Ambulator

Call growth

Monthly Calls by Priority

0

1000

2000

3000

4000

5000

6000

7000

8000

2000 2001 2002 2003 2004 2005

Priority CAGR

1

2

3

4

5-7

10%

10%

3%

10%

6%

Growth is continuing at around 10% per year.

Calls

Priority one calls

Other29%

Cardiac18%

Convulsions6%

Unconscious12%Traffic

12%

Fall6%

Breathing17% Breathing

Cardiac

Unconscious

Traffic

Fall

Convulsions

Other

CAGR

5%5%

0%1%1%6%20%

The largest categories within priority one are growing more slowly

Call forecasting

0

2

4

6

8

10

12

0.00 4.00 8.00 12.00 16.00 20.00 24.00

Average Hourly Calls

An average of ~180 calls arrive in one day.

Call forecasting

• But daily totals vary due to other effects (weather, etc.)

020406080

100120140160

120 140 160 180 200 220 240 260

Distribution of Daily Calls

Number of Calls

Frequency

Call ForecastingCalls arrive in distinct patterns

Sun Mon Tues Wed Thurs Fri Sat

Weekly Call Pattern

Jan Apr Jul Oct

Yearly Call Pattern (Detrended)

Geo-codingCall Locations Dispatch Locations

Where Are Calls Overgoal (X

(X

(X

(X

(X

(X

(X

(X

(X

(X

(X(X

(X

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2821

15 17

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18

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68

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1120

1424

9

19

2630

Station Zones 2004

(X(X

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2821

15 17

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1120

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2630250

180

90

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Legend:

Late Calls

Year 2000

2000 2001 2002 2003 2004 2005

(X(X

(X

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(X

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(X

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(X(X

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2630250

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Legend:

Late Calls

Year 2001

2000 2001 2002 2003 2004 2005

(X(X

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(X(X

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Legend:

Late Calls

Year 2002

2000 2001 2002 2003 2004 2005

(X(X

(X

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(X

(X

(X

(X

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(X(X

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1120

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9

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2630250

180

90

30

Legend:

Late Calls

Year 2003

2000 2001 2002 2003 2004 2005

(X(X

(X

(X

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(X

(X

(X

(X

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(X(X

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2821

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12

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12

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1120

1424

9

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2630250

180

90

30

Legend:

Late Calls

Year 2004

2000 2001 2002 2003 2004 2005

#S#S

#S

#S

#S

#S

#S

#S

#S

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#S#S

#S

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#S31

3026

19

9

1424

12

22

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1120

4

12

6

8

18

5

1715

2821

250

180

90

30

Legend:

Late Calls

Year 2005

2000 2001 2002 2003 2004 2005

Why Calls are Overgoal

Evaluate each call to see why it was overgoal:1. Chute problem2. Dispatch problem3. Call Evaluation problem4. Hospital problem5. Travel distance (from nearest two stations)6. Stations busy (nearest two)7. Driving problem (other travel factors)8. Multiple problems (combinations of the above)

Why Calls are Overgoal

0

500

1,000

1,500

2,000

2,500

2000 2001 2002 2003 2004 2005

Call Eval ProblemDispatch ProblemHospital ProblemDriving ProblemDistance ProblemChute ProblemBusy-ness ProblemMultiple Problem

Total Late Calls by Year(Responses > 8 Minutes)

LateCalls

Hospital time

0

500

1,000

1,500

2,000

2,500

3,000

3,500

Jan

-00

Ma

y-0

0

Se

p-0

0

Jan

-01

Ma

y-0

1

Se

p-0

1

Jan

-02

Ma

y-0

2

Se

p-0

2

Jan

-03

Ma

y-0

3

Se

p-0

3

Jan

-04

Ma

y-0

4

Se

p-0

4

Jan

-05

Ma

y-0

5

Foothills

Lougheed

Rockyview

Children's

Time spent waiting at the hospital nearly tripled from 2000-2005Hours/Month

Data from 2000-2005 – all calls.

So what did they do?

• Lobby the Health Authority• Put paramedics in the hallway• Created drop-off protocol “Full Capacity Protocol”• Divert drop-offs to less busy hospitals

UNSUCCESSFUL

UNSUCCESSFUL

MIXED

???

St. Albert Station Location

St. Albert Fire – Where do we put new stations through 2015?

• 2003 Population - ~50,000• 2 station• Combined fire/EMS• ~2500 calls per year

Stochastic Model – Travel Times

Speed Calculations

•We derived a ‘most-likely route’

•We calculated distances using this routing scheme

•Using actual travels times, we calculated average speeds for 3 types of roads:

SecondaryPrimary

Residential

50 km/h 40 km/h 32 km/h

1.5 km

2 km

0.5 km

1.5 km

1.5 km

Stochastic model – travel timesAlthough covered on average – this node is not reached 2% of the time in 9 minutes

0

10

20

30

40

50

60

70

Actual Travel Times for 34 Mission Ave (361 Incidents)

Stochastic model – travel time distributions

0.00%

2.00%

4.00%

6.00%

8.00%

10.00%

12.00%

Modeled Travel Time (mean = 3 min.)

0

10

20

30

40

50

60

70

Actual Travel Times for 34 Mission Ave (361 Incidents)

Coverage analysis – probabilistic coverage

0%

1%

2%

3%

4%

5%

6%

7%

8%

9%

Total Time(Dispatch Time + Activation Time + Travel Time)

Nine minute target response

98% of the instances are to the left of 9 min

Coverage analysis – probabilistic coverage

0%

1%

2%

3%

4%

5%

6%

7%

8%

9%

Total Time(Dispatch Time + Activation Time + Travel Time)

Nine minute target response

69% of the instances are left of 9 minutes

Stochastic model

Station

Illustrative

Covered in 9 min 95% of the

time

Covered in 9 min 80% of the

time

Covered in 9 min 60% of the

time

X

X

X

120 calls

130 calls

40 calls

=

=

Total Calls: 290

=

114 calls

104 calls

24 calls

Total Covered: 242

Coverage Rate = 83%

Current (2002)

Primary

Secondary

Residential

2005

Primary

Secondary

Residential

2010

Primary

Secondary

Residential

2015

Primary

Secondary

Residential

Station Recommendations2002 2005 2010 (2015)

2005 2010 2015Calls 92.9% 91.2% 91.8%EMS 93.1% 91.5% 92.0%FIRE 92.0% 90.3% 90.8%

VALUE 91.8% 89.9% 90.4%

Expected Performance

% Calls Covered in 9 Minutes

Year

Coverage Through Time in St. Albert (projected calls)

80%

82%

84%

86%

88%

90%

92%

94%

2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

80%

82%

84%

86%

88%

90%

92%

94%

2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

80%

82%

84%

86%

88%

90%

92%

94%

2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

North station added

West station added

Calgary

Results

• Mayor: “This is all well and good – percent improvements here and there – but what does this mean in terms of lives saved?

Response Times vs. Heart Attack Mortality

Adding Stations – Heart Attacks

Number of Heart Attack

Fatalities

Year

Expected Heart Attack Fatalities

12

14

16

18

20

22

24

2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

12

14

16

18

20

22

24

2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

West station added

East station added

On average, we save an additional life every year.

Models

Covering

(1974) (1983)

(1987)

Uncertain ambulance availability

Uncertain response time

Budge,Ingolfsson,

Erkut(2005)

Base demand(P1 + all)

Growth FactorDay of Week

indexHour of Week by

Region (GC)DEMAND

Drive times base Growth Factor Rush hour index DRIVE TIMES

Chute average base

Chute average index (hour by stn)

CHUTE AVERAGE

Chute variance base

Chute variance index (hour by stn)

CHUTE VARIANCE

Busy times base Growth factor Busy times indexindex

RESPONSE TIMES

RESPONSE TIMES STDEV

STATION BUSY TIMES

STATION PREFERENCE

RESPONSE PROBABILITY

CALCULATION ENGINE

COVERAGE STATISTICS

Response cut off (n)

Servers (ambulances)

Date/Time Input

Month of Year by Region

(GC)

Hour of Day Index

ASSIG PROB

The Ambulator Model

Forecast Engine

Input

•Date/Time

Output

•Call Demand

•Busy Time

•Response Time

Calculation Engine

Input

•Forecast outputs

•Unit Deployment

Output

•Coverage

Deployment Engine

Input

•Available units

Output

•“Optimal” deployment

Scheduling Engine

Input

•Optimization output

•Available shifts

Output

•Optimal schedule

Forecastor Calculator Tabu Solver Schedulator

15 minutes to

Calculate

4,000 hours to calculate

2 minutes to calculate

Hospital Time Improvement

65%

70%

75%

80%

85%

90%

0:00 3:00 6:00 9:00 12:00 15:00 18:00 21:00

Performance w/ 30 Minute HT

Performance w/ Current HT

Total Improvement of 2.3%

Performance(Average total daily performance)

Adding P1 Calls

70%

72%

74%

76%

78%

80%

0% (0 calls) 5% (745 calls) 10% (1489calls)

15% (2234calls)

20% (2979calls)

Additional P1 Calls

Co

veag

e

(July – 2005)

~1% drop per 1000 Calls

65%

70%

75%

80%

85%

0:00 3:00 6:00 9:00 12:00 15:00 18:00 21:00

Baseline 2005

New Schedule

Schedule & Fleet Size

20

25

30

35

40

45 Baseline

New Schedule

Note – schedule and performance based on 2004.

Old vs. Optimal Schedule - 2005

Performance

76.8%78.2%

Total

Total Improvement of 1.4%

Shift FlexibilityHow does performance change when we adjust shift flexibility?

Performance

Unlimited fleet size

Fleet SizeHow does performance change when we adjust fleet size?

60.0%

61.0%

62.0%

63.0%

64.0%

65.0%

66.0%

67.0%

68.0%

69.0%

70.0%

44 43 42 41 40 39 38 37 36 35 34 33 32

876 Unit Hours

828 Unit Hours

780 Unit Hours

Fleet Size

Performance

1hr shifts

Unit HoursAn additional 24hr unit is 0.8% improvement at 780 unit hours

60%

61%

62%

63%

64%

65%

66%

67%

68%

69%

70%

71%

72%

720 744 768 792 816 840 864 888 912 936 960

Unit Hours

Performance

Station Expansion

(X(X

(X

(X

(X

(X

(X

(X

(X

(X

(X(X

(X

(X

(X

(X

(X

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(X

(X

(X

2821

15 17

5

18

31

22

12

4

12

68

23

1120

1424

9

19

2630

+1+2

Total Improvement of

~1%

Additional Discoveries

From the Model• Adding the next best station – 1% improvement• Adding additional unit to the best station – 0.8%

improvement• Cutting patient time (scene, transport, or hospital) by 4

minutes – 1%From the Data• Cutting response time (call eval, dispatch, chute, or

travel) by 6 seconds yields 1%• Own-zone performance shows if stations are

necessary…or perhaps call eval/dispatch/chute improvements

Where are we going?

0.0%

0.5%

1.0%

1.5%

2.0%

2.5%

3.0%

3.5%

Performance Improvement

Cost

Improved Dispatch

Additional Crews

Additional Stations

Expanded Stations

Things learned

• Pretty counts• Quick is as important as accurate• Data analysis is often more beneficial than modeling• Rules of thumb